improving the simplified level 2 prototype processor for
TRANSCRIPT
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Improving the Simplified Level 2 Prototype Processor
for Retrieving Canopy Biophysical Variables from
Sentinel 2 Multispectral Instrument DataRichard Fernandes1, Fred Baret2, Luke Brown3, Francis Canisius1, Jadu Dash3, Najib Djamai1,
Gang Hong1, Camryn MacDougall1, Hemit Shah1, Marie Weiss2, and Detang Zhong1
1Canada Centre for Remote Sensing2INRA France
3Southampton University
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Essential Variables
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Sentinel 2 Mission Product Requirements
Threshold
Accuracy
15% 25%,0.75
20%,0.10
20%,0.1010%,0.05
10%,0.05
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Simplified Level 2 Prototype Processor (SL2P)4
S2Toolbox ≠ MATLAB = Google Earth Engine
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
LEAF-Toolbox (Landscape Evolution and Forecasting)5
Cell Phone/Google Earth Engine/Python
Multi Layer Neural NetworkUser specified via CSV files per Land Cover
Process 1000 granules<2minutesArbitrary spatial subsettingPer granule or mosaic outputExport to Google Drive
Parsing utility for SL2P Matlab outputUser specified sensor collectionhttps://rfernand387.users.earthengine.app/view/leaf-toolbox-sl2p
https://code.earthengine.google.com/bb6f7efc2cd7dc30189505d7e303c565
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
100m Monthly Mosaics of Canada6
LAI SL2PAugust 2020
FCOVER SL2PAugust 2020
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Sample Validation Results
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Automated open source validation using GEE 8
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
9
• Can we reduce SL2P LAI, fAPAR and FCOVER uncertainty over forests?
• Hypotheses:
– H(0): SL2P, global database + PROSAIL
– H(a): Land cover database + PROSAIL
– H(b): Land cover/species database + FLIGHT
• Test over NEON and CCRS sites
Research Questions
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Species cover based PROSAIL parameters. Modal LAI ~4. Clumping from canopy architecture.DeciduousBroadleafForestCalibration
Hetrogenous discrete RT model.
Land cover based PROSAIL parameters. Modal LAI ~4. Clumping from clumping index.DeciduousBroadleafForestCalibration
Heterogenous turbid RT model.
Global in-situ PROSAIL parameters. Modal LAI ~2. No clumping.
DeciduousBroadleafForestCalibration
Homogenous turbid RT model.
H(0): SL2P H(a): CCRS-SAIL H(b): CCRS-FLIGHT
Treatments
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H(0): SL2P H(a): CCRS-SAIL H(b)CCRS-FLIGHT
Results-LAI
Needleaf ForestBroadleaf ForestMixed ForestSymbol size proportional to clumping
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
H(0): SL2P H(a): CCRS-SAIL H(b)CCRS-FLIGHT
Results-fAPAR
Needleaf ForestBroadleaf ForestMixed ForestSymbol size constant (not proportional to clumping)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Discussion• In-situ, algorithm standard errors sufficient to test hypotheses for large (>300)
sample sizes
• Google Earth Engine facilitates large area validation (using LEAF-Toolbox)
• LAI: – SL2P biased LAI>2 due to lack of clumping– SL2P+clumping decreased bias but increased precision error vs SL2P– FLIGHT+species lower bias even greater precision error than SL2P
• fAPAR– SL2P and SL2P+clumping similar– FLIGHT+species reduces uncertainty but increased of bias
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Recommendations
• LAI<2 and fAPAR: H(0) SL2P recommended
• LAI>2 H(a) SL2P+clumping recommended due to low H(b) precision
• How can we increase H(b) precision LAI>2– Other inversion algorithms– Ancillary datasets, high res imagery to constrain clumping– Temporal smoothing to reduce uncertainty due to input error– Calibration using in-situ reference measurements
• SL2P+clumping should be implemented and compared to MODIS and CGLS